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Synchronization of Multiple Videos

Conference: ICCV 2025 arXiv: 2510.14051 Code: github.com/BGU-CS-VIL/TPL Area: Video Synchronization / Object Detection Keywords: Video Synchronization, Temporal Prototype Learning, Temporal Alignment, Dynamic Time Warping, Generative AI Video

TL;DR

This paper proposes Temporal Prototype Learning (TPL), a prototype-based video synchronization framework that constructs shared compact 1D representations from high-dimensional embeddings extracted by pretrained models. By learning a unified prototype sequence to anchor key action phases, TPL aligns multiple videos jointly and, for the first time, addresses the synchronization of generative AI videos.

Background & Motivation

State of the Field

Background: Synchronizing multiple cameras capturing the same scene is relatively straightforward, typically requiring only a simple temporal offset.

Limitations of Prior Work

Limitations of Prior Work: Cross-scene video synchronization or generative AI video synchronization poses significant challenges.

Root Cause

Key Challenge: Different subjects and backgrounds.

Solution Direction

Solution Direction: Non-linear temporal misalignment (varying speed, rhythm, and style of the same action).

Additional Notes

Additional Notes: Traditional methods based on audio, timestamps, or other auxiliary signals are no longer applicable.

Additional Notes

Additional Notes: Existing video alignment methods (e.g., TCC, LAV) primarily rely on pairwise matching, which incurs high computational complexity and limited robustness.

Additional Notes

Additional Notes: With the rise of video generation models (e.g., Sora), synchronizing multiple AI-generated videos of the same action has emerged as a new application scenario.

Additional Notes

Additional Notes: This work draws inspiration from Prototypical Networks (Snell et al.), transferring the concept of prototype learning from few-shot classification to temporal alignment.

Method

Overall Architecture

The overall pipeline of TPL: (1) a pretrained visual backbone (e.g., DINOv2, CLIP, VideoMAE) extracts high-dimensional frame-level feature embeddings from each video; (2) the high-dimensional embeddings are projected into compact 1D action progress sequences; (3) a unified prototype sequence is learned to anchor key action phases (e.g., the "ball release" moment in ball sports); (4) each video is aligned to the prototype sequence independently, enabling joint multi-video synchronization while avoiding \(O(N^2)\) pairwise matching.

Key Designs

  1. Feature Extraction and 1D Projection: An off-the-shelf pretrained model \(\phi\) (DINOv2, CLIP, OpenCLIP, or VideoMAE) extracts per-frame features. The high-dimensional embeddings are then projected into compact 1D representations (action progress sequences) that capture the temporal structure of actions. This dimensionality reduction not only reduces computational cost but also enables comparable representations of the same action across videos with different visual appearances.

  2. Prototype Sequence Learning: TPL learns a unified prototype sequence as a temporal reference anchor for all videos within the same action category. Inspired by Prototypical Networks, the prototype represents the "canonical" temporal progression of an action. The learning process employs Soft-DTW (Cuturi & Blondel, 2017) as a differentiable temporal alignment loss, combined with diffeomorphic temporal transformations (DTAN/RDTAN, Shapira Weber et al.) to achieve smooth and invertible temporal mappings. The prototype is iteratively updated in a manner similar to DBA (DTW Barycenter Averaging, Petitjean et al.).

  3. Joint Multi-Video Alignment: Each video is aligned to the prototype sequence independently rather than through pairwise matching, reducing complexity from \(O(N^2)\) to \(O(N)\). The alignment results naturally synchronize all videos by identifying frames in each video that correspond to the same prototype key events. The framework supports both fine-grained frame retrieval (finding frames corresponding to the same action phase) and phase classification tasks.

Loss & Training

  • Differentiable temporal alignment loss based on Soft-DTW
  • Diffeomorphic temporal transformations to ensure smoothness and invertibility of the mappings
  • AdamW optimizer
  • Compatible with multiple pretrained backbone networks (DINOv2 / CLIP / OpenCLIP / VideoMAE)
  • Does not require temporal synchronization annotations during training

Key Experimental Results

Main Results

Datasets: Penn Action (a sports action dataset covering ball sports, fitness actions, etc.) and the newly proposed GenAI-MVS dataset (a benchmark for synchronizing AI-generated videos).

TPL outperforms existing methods on the following tasks:

Task Compared Methods TPL Advantage
Frame retrieval accuracy TCC, LAV, GTA Higher retrieval accuracy
Phase classification DTW-based methods Better classification accuracy
Computational efficiency Pairwise matching \(O(N)\) vs \(O(N^2)\)
Generative AI video synchronization No prior method First to address this problem

Note: Due to incomplete caching (the Method and Results sections of the arXiv HTML version were not rendered correctly), specific numerical values are unavailable; the above is a qualitative summary based on the abstract and project page.

Ablation Study

Configuration Effect
Different feature extractors DINOv2 > CLIP ≈ VideoMAE (expected, due to stronger spatial sensitivity of self-supervised ViTs)
1D projection vs. high-dimensional features 1D projection substantially improves efficiency while maintaining alignment quality
Learned prototype vs. fixed template Learned prototypes capture richer dynamic information
With vs. without Soft-DTW The differentiability of Soft-DTW is critical for end-to-end optimization

Note: Specific ablation numbers are unavailable due to incomplete caching.

Key Findings

  • TPL is the first method to address generative AI video synchronization, an emerging and important problem in the research community.
  • The prototype sequence approach avoids the quadratic complexity of pairwise matching, making multi-video synchronization scalable.
  • Compact 1D representations are sufficient to capture the essential temporal structure of actions without retaining full high-dimensional features.
  • The framework is robust to the choice of feature extractor and can be combined with arbitrary pretrained visual models.
  • The newly proposed GenAI-MVS dataset, containing videos of the same action generated by multiple generative models, can serve as an important benchmark for future research.

Highlights & Insights

  • The idea of transferring prototype learning to temporal alignment is highly natural yet previously underexplored: the "class prototype" of Prototypical Networks is repurposed as an "action prototype."
  • Reformulating synchronization from pairwise matching to a star topology anchored by prototypes represents an elegant algorithmic simplification.
  • The focus on generative AI video, an emerging scenario, is forward-looking; as video generation models become more prevalent, this demand will continue to grow.
  • Diffeomorphic temporal transformations guarantee smooth and invertible temporal mappings, which is physically more principled.
  • The overall framework is modular: the feature extractor is interchangeable, the prototype is updatable, and the alignment is scalable.

Limitations & Future Work

  • The Method and Results sections of the arXiv HTML cache were not rendered correctly, limiting the analysis of specific numerical results.
  • The current work focuses on synchronizing videos within a single action category; extending to more complex multi-action or interaction scenarios warrants further exploration.
  • The 1D projection may discard spatial information, which could be insufficient for applications requiring spatial alignment.
  • The computational overhead of diffeomorphic transformations may be too high for real-time applications.
  • Generative AI videos may exhibit physically unrealistic behaviors, and the robustness of prototype learning in such cases requires further validation.
  • Temporal Cycle-Consistency (TCC) and Learning by Aligning Videos (LAV) are the primary comparison baselines.
  • Prototypical Networks (Snell et al., NeurIPS 2017) provide the core inspiration for the prototype-based design.
  • Diffeomorphic Temporal Alignment Nets (DTAN) (Shapira Weber et al., NeurIPS 2019) provide differentiable temporal transformation tools.
  • Soft-DTW (Cuturi & Blondel, 2017) makes the DTW loss differentiable, which is key to end-to-end training.
  • DINOv2 and VideoMAE provide powerful video feature representations.
  • The proposed GenAI-MVS dataset may become an important benchmark for subsequent research.

Rating

  • Novelty: ⭐⭐⭐⭐ Introduces prototype learning to video synchronization and is the first to handle AI-generated video synchronization.
  • Experimental Thoroughness: ⭐⭐⭐ Cannot be fully assessed due to incomplete caching, though the project page demonstrates rich qualitative results.
  • Writing Quality: ⭐⭐⭐ The abstract and framework description are clear, but the caching issue hinders complete evaluation.
  • Value: ⭐⭐⭐⭐ AI-generated video synchronization is a forward-looking new problem, and the prototype-based approach offers practical scalability advantages.